ArticleSensors (Basel, Switzerland)2018
Personalized Physical Activity Coaching: A Machine Learning Approach.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Digital Interventions on Healthy Lifestyle Management: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Experiences of mobile health in promoting physical activity: A qualitative systematic review and meta-ethnography.PloS one · 2018Pooled it
- The influence of a consumer-wearable activity tracker on sedentary time and prolonged sedentary bouts: secondary analysis of a randomized controlled trial.BMC research notes · 2018Trial
- Understanding Multilevel Correlates of Long-Term Physical Activity Trajectories Among Middle-Aged and Older Adults: A Machine-Learning Analysis.Behavioral sciences (Basel, Switzerland) · 2026Article
- The Evolution of Machine Learning Algorithms and Their Contribution to Physical Activity Management.Advances in experimental medicine and biology · 2026Review
- Digital health solutions for chronic disease physical activity management: wearable devices, artificial intelligence, and public health implementation.Frontiers in public health · 2026Review
- A hybrid q-rung linear diophantine fuzzy WASPAS approach for artificial intelligence algorithm selection in physical education.Scientific reports · 2025Article
- Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization Overview.Journal of medical Internet research · 2024Article
- Approaches for the Use of AI in Workplace Health Promotion and Prevention: Systematic Scoping Review.JMIR AI · 2024Article
- Visio-spatial intelligence skills in non-athletes versus amateur boxers.Medical hypothesis, discovery & innovation ophthalmology journal · 2024Article
- AI and semantic ontology for personalized activity eCoaching in healthy lifestyle recommendations: a meta-heuristic approach.BMC medical informatics and decision making · 2023Article
- An automatic and personalized recommendation modelling in activity eCoaching with deep learning and ontology.Scientific reports · 2023Article
- Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review.JMIR medical informatics · 2023Review
- A theory-based and data-driven approach to promoting physical activity through message-based interventions.Frontiers in psychology · 2023Article
- Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation.Scientific reports · 2022Article
- Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data.Sensors (Basel, Switzerland) · 2022Article
- Classification of Parkinson's disease and its stages using machine learning.Scientific reports · 2022Article
- Personalized Recommendations for Physical Activity e-Coaching (OntoRecoModel): Ontological Modeling.JMIR medical informatics · 2022Article
- The effect of two COVID-19 lockdowns on physical activity of school-age children.Sports medicine and health science · 2022Article
- Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88-0.99, and mean F1-score = 0.90, range = 0.87-0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.
Indexed as
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.